The study will be based on a quantitative research design; this is because quantitative methods seek to confirm a hypothesis about a particular phenomenon, unlike qualitative designs that explore, explains, and tries to understand an event. Various studies conducted by different scholars such as Harper, Lynch & Smith (2011), Jeemon & Reddy (2010), and Kreatsoulas & Anand (2010) already confirmed that there is a link between social determinants and the development of heart diseases. Therefore, this study aims to verify to what extent social determinants contribute to the development of heart diseases in low-income communities. If previous studies had not found any relation between these two variable (social determinants of health and heart diseases), then it would have been appropriate to use qualitative research methodology. Accredited organizations such as the World Health Organization through a survey conducted by Wilkinson& Marmot (2003), also confirmed a similar relationship. To be specific, this analysis would be a cohort study since it is based on a large group of individuals who share a common characteristic within a defined period (low-income community). Krueger (2018) stipulates that the low-income earners in any society are the largest cohort compared to any other social stratum group, which are the middle and high-income earners. Since this study deals with low-income earners, then it means that it would touch on the majority in the respective community. Systematic sampling is efficient when dealing with such a large population. This sampling technique involves a probability method in which the respondents are members of a broader community, which in this case fall under the framework of low-income earners, and are chosen according to an unsystematic beginning point and a defined intervallic sequence. In spite of the sample respondents being pre-determined, systematic sampling is thought to be unsystematic if the intervallic interval is established prior, and the starting point is selected randomly. The first step towards conducting a systematic sampling will be to assign numbers to every element of the required population (this can be from 1 to 100 or 100; for the sake of this explanation, it will be 1 to 100). The second step is dividing the population as per the required sample size. For instance, since the community is 100, the sample size is 10, 100/10= 10. (Gentles, Charles, Ploeg, & McKibbon, 2015). This means that the researcher would use all the respondents that fall in the 10 th position (10, 20, 30, 40, 50, 60, 70, 80, 90, and 100). The research would utilize both primary and secondary data. Primary data will be derived from structured questionnaires with close-ended questions to get quantifiable responses. The researcher would administer the structured questionnaire through face-to-face interviews, telephone interviews, and e-mail questionnaires. The second tool will be the use of observations and recording well-defined events, for example, collecting the number of patients suffering from heart conditions from the sampled population. Lastly, the research would obtain data from various management information systems. Besides primary data, the study would use the findings and conclusions from multiple studies conducted in previous years to forge a formidable argument and counter-arguments, when establishing the conclusion and recommendations of the review. However, when using secondary data, the study would limit itself to peer-reviewed articles, which are credible and have been published within the last ten years to avoid using outdated information.
References
Gentles, S. J., Charles, C., Ploeg, J., & McKibbon, K. A. (2015). Sampling in qualitative research: Insights from an overview of the methods literature. The Qualitative Report , 20 (11), 1772
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Harper, S., Lynch, J., & Smith, G. D. (2011). Social determinants and the decline of cardiovascular diseases: understanding the links. Annual review of public health , 32 , 39-69. [Online] Available at; http://search.ror.unisa.edu.au/record/UNISA_ALMA51108428620001831/media/digital/open/9915909355501831/12143970230001831/13143997160001831/pdf . Retrieved on [June 17, 2018].
Jeemon, P., & Reddy, K. S. (2010). Social determinants of cardiovascular disease outcomes in Indians. The Indian journal of medical research , 132 (5), 617.
Kreatsoulas, C., & Anand, S. S. (2010). The impact of social determinants on cardiovascular disease. Canadian Journal of Cardiology , 26 , 8C-13C.
Krueger, A. B. (2018). Inequality is too much of a good thing. In The Inequality Reader (pp. 25-33). Routledge.
Wilkinson, R. G., & Marmot, M. (Eds.). (2003). Social determinants of health: the solid facts . World Health Organization. [Online] Available at; http://www.wpro.who.int/health_research/documents/dhs_hr_health_in_asia_and_the_pacific_07_chapter_2_social_determinants_of_health.pdf . [June 17, 2018].
